The communications revolution of the late 20th and early 21st century has fuelled a need for better, faster, and more useful communications devices. Currently, there is a need for more efficient and more effective methods for determining the parameters of incoming wireless signals. The need is most acute in the wireless communications industry but such technology can also be applied to military uses.
Previously, to ensure proper determination or estimation of the parameters of an incoming signal, various antenna arrays have been used in conjunction with many varied methods. Some of the previous work in this field are as follows, all of the following being hereby incorporated by reference:    H. L. Van Trees, Optimum Array Processing, Part IV of Detection, Estimation, and Modulation Theory, 1st ed., John Wiley Inc, 2002.    S. Charndran, Advances in Direction of Arrival Estimation, Artech House, 2006.    R. O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, Vol. AP-34, No. 3. 1986, pp. 276-280, March 1986.    R. Roy, T. Kailath, “ESPRIT-Estimation of Signal Parameters Via Rotational Invariance Techniques” IEEE Transaction on Acoustics, Speech and Signal Processing, VOL. 37, NO. 7, 1989.    B., Ottersten, M. Viberg, and T. Kailath, “Performance analysis of the total least squares ESPRIT algorithm,” IEEE Transactions on Signal Processing, vol. 39, no. 5, May 1991.    A. L. Swindlehurst, B. Ottersten, R. Roy, T. Kailath, “Multiple invariance ESPRIT,” IEEE Transactions on Signal Processing, vol. 40, no. 4, April 1992.    Y. L. Jong and M. Herben, “High-resolution angle of arrival Measurement of the mobile radio Channel,” IEEE Trans. Antennas Propagat., vol. 47, no. 11, pp. 1677-1687, November 1999.    Jong, Yvo L. C. de (2001) Measurement and Modeling of Radio wave Propagation in Urban Microcells, PhD Thesis, Department of Electrical Engineering, University of Technology (EUT), Netherlands.    A. Broumandan, T. Lin, A. Moghaddam, D. Lu, J. Nielsen, G. Lachapelle, “Direction of Arrival Estimation of GNSS Signals Based on Synthetic Antenna Array,” ION GNSS 2007, Fort Worth, Tex., 25-28 Sep. 2007.    J. Pierre, M. Kaveh, “Experimental Performance of Calibration and Direction-Finding Algorithms,” Acoustics, Speech, and Signal Processing ICASSP, 1991.    C. M. S. See, “Sensor Array Calibration in the Presence of Mutual Coupling and Unknown Sensor Gains and Phases,” Electronics Letters, IEEE, Vol. 30, No. 5, March 1994.    F. Li, R. J. Vaccaro, “Sensitivity Analysis of DOA Estimation Algorithms to Sensor Errors,” IEEE Transactions on Aerospace and Electonic Systems, Vol. 28, No. 3 Jul. 1992.    A. L. Swindlehurst, T. Kailath, “A Performance Analysis of Subspace-Based Methods in the Presence of Model Errors, Part I: The MUSIC Algorithm,” IEEE Transactions on Signal Processing, Vol. 40, No. 7, July 1992.    J. C. Liberti JR. and Theodore S. Rappaport, Smat Antennas for Wireless Communication. Prentice Hall TPR, 1999.    S. M. Kay, Fundamentals of Statistical Processing, Volume I: Estimation Theory, Prentice Hall, 1993.    M. Wax, T. Kailath, “Detection of Signals by Information Theoretic Criteria,” IEEE Transactions on Acoustics, Speech, Signal Processing, Vol. ASSP-33, pp. 387-392, 1985.    J. J. Caffery, G. L. Stuber, “Subscriber Location in CDMA Cellular Network,” IEEE Transactions on Vehicular Technology, Vol. 47, No. 2, 1998.    J. J. Caffery, Wireless Location in CDMA Cellular Radio Systems. Kluwer Academic Publishers, Boston, 2000.    A. Moghaddam, Enhanced Cellular Network Positioning Using Space-Time Diversity. MSc Thesis, Department of Geomatics Engineering, The University of Calgary, Calgary, Canada, 2007.    B. Allen, M. Ghavami, Adaptive Array Systems Fundamentals and Applications, John Wily and Sons, Ltd, 2005.    A. Broumandan, T. Lin, A. Moghaddam, D. Lu, J. Nielsen, G. Lachapelle, “Direction of Arrival Estimation of GNSS Signals Based on Synthetic Antenna Array,” ION GNSS 2007, Fort Worth, Tex., 25-28 Sep. 2007.    R. Roy, T. Kailath (1989) “ESPRIT-Estimation of Signal Parameters Via Rotational Invariance Techniques” IEEE Transaction on Acoustics, Speech and Signal Processing, VOL. 37, NO. 7    E. Gonen, M. Mendel, “Application of Cumulants to Array Processing-Part III: Blind Beamforming for Coherent Signals,” IEEE Transactions on Signal Processing, Vol. 45, No. 9, September 1997.    J. Jones, P. Fenton, B. Smith, “Theory and Performance of the Pulse Aperture Correlator,” Proceedings of ION GPS, 2004.    Parsons, J. D.: ‘The Mobile Radio Propagation Channel’, (John Wiley & Sons LTD, 2nd ed. 2000)    Rensburg, C., and Friedlander, B.: ‘Transmit Diversity for Arrays in Correlated Rayleigh Fading’, IEEE Trans. Vehicular Tech., Vol. 53, No. 6, pp. 1726-1734, November 2004    Kim, S.: ‘Acquisition Performance of CDMA Systems with Multiple Antennas’, IEEE Trans. Vehicular Tech., Vol. 53, No. 5, pp. 1341-1353, September 2004    Choi, S. and Shim D.: ‘A Novel Adaptive Beamforming algorithm for a smart antenna system in a CDMA mobile communication environment’, IEEE Trans. Vehicular. Tech., Vol. 49, No. 5, pp. 1793-1806, September 2000    Hyeon, S., Yun, Y., Kim, H. and Choi, S.: ‘Phase Diversity for an Antenna-Array System with a Short Interelement Separation’, IEEE Trans. Vehicular Tech., Vol. 57, No. 1, pp. 206-214, January 2008    Kay, S. M.: ‘Fundamentals of Statistical Signal Processing Detection Theory’ (Prentice-Hall, Inc, 1998)    Fulghum, T. L., Molnar, K. J. and Duel-Hallen, A.: ‘The Jakes Fading Model for Antenna Arrays Incorporating Azimuth Spread’, IEEE Trans. Vehicular Tech., Vol. 51, No. 5, pp. 968-977, September 2002    Liberti, J. and Rappaport, T. S.: ‘Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications, Prentice Hall, 1999)
While antenna arrays have been found to be useful, the size of multi-element antenna arrays preclude the use of such devices in current devices. Smaller systems would be useful and can be deployed in current handheld devices.
In signal detection applications, an incoming signal used in terrestrial or indoor wireless communication links typically propagates from the transmitter to a receiver over multiple reflective paths with a with a consequence of a random variation in the complex amplitude of the received signal. When the receiving antenna is located in a diffuse multipath scattering environment, fading appears to be a random function of antenna location conforming approximately to Rayleigh fading statistics with spatial decorrelation intervals of less than the carrier wavelength of the signal. If the receiver uses a single stationary antenna, then a substantial fading margin is required to ensure reliable signal detection. To reduce the fading margin required, the receiver can use multiple spatially separated antennas that exploit either the spatial diversity or beamforming abilities that are inherent properties of discrete antenna arrays. As noted above, multiple element antenna arrays are incompatible with current devices due to their physical size.
One parameter of incoming signals that can be critical is time of arrival. Time Of Arrival (TOA) of a signal is a fundamental observable in most positioning applications. The position of the mobile station (MS) in 3-dimension space can be estimated by four or more independent TOA measurements from base station transmitters that are spatially separated with known locations in the vicinity of the MS. However, the coexistence of the multipath components along with the desired line of sight (LOS) signal component typically causes large errors in the estimation of the TOA observables by the MS which maps into large positional errors. CDMA signaling has a practical advantage of a sizeable bandwidth which allows for partial resolution of the LOS and corrupting multipath components. However, TOA measurement errors on the order of 1 μsec are commonly encountered which typically result in positional errors of several hundred meters. To meet the requirements of applications that require accurate position estimation on the part of the MS, lower deviation and bias of the TOA observables is required. To achieve this requires mitigation of the distortions caused by the existence of the multipath components.
Significant research efforts have been expended on using spatial information from multiple receiver antennas. Classical beam forming and null steering algorithms have been explored which are effective but require an antenna array consisting of multiple antennas which does not fit the form factor of the handheld communications device. In addition, the additional analog signal processing is a limiting factor in this context. There is therefore a need for a solution that has the advantages of antenna array processing but without the unwieldy hardware implications of a multi-antenna array. One option would be a synthetic array consisting of a single low gain antenna conformal with the physical constraints of the handheld MS device. Spatial array processing techniques for single antenna synthetic arrays have been deployed for several decades however, these methods require that the antenna be translated through a trajectory known to the receiver with very good precision. Incorporation of such solutions into a communications handset would require a precision measurement capability in the form of an inertial device. Such an inertial device would be difficult to implement into a handheld device.
Another problem of interest in many signal-processing applications is the estimation of signal parameters from a set of data measurements. High-resolution Angle Of Arrival (AOA) estimation is an important issue in many applications such as radar, sonar, spatial filtering and location estimation specifically enhances the 911 requirement (E-911) in wireless emergency services. There have been several high-resolution AOA estimation methods including the multiple signal classification (MUSIC) and the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithms. Although the MUSIC algorithm is widely used, it has certain practical implementation issues when compared with ESPRIT. The MUSIC algorithm requires prior calibration of the antenna elements such as the phase, gain as well as the positions of the elements. In addition, a computationally expensive search is required over the processed parameter space. AOA estimation with the MUSIC algorithm requires the exact knowledge of position of the elements. However, the specific array geometry of standard ESPRIT algorithm requires twice the number of sensors in comparison with the MUSIC algorithm.
In particular, applications such as handset-based geolocation estimation and determining direction of interfering signals, portability of receiver is a primary issue generally precluding the use of several antenna elements as required for AOA estimation. To overcome this restriction, antenna array can be synthesized by moving antennas in an arbitrary trajectory. Some researchers have shown an application of using synthetic array with uniform circular array (UCA). They have used UCA-MUSIC based on phase-mode excitation with beam-space processing to determine multipath contributions in wireless mobile propagation environments. In one research implementation, a mechanical lever arm was used to synthesize a circular array by using a single rotating antenna with the constant speed. AOA estimation using a synthetic array has significant advantages because inter channel phases, and gains and mutual coupling between antenna elements do not affect the AOA estimation. However, the basic assumption of synthetic arrays with the MUSIC algorithm, the stationarity of the radio channel, is not always possible in real mobile communication systems.
Several methods have been developed to implement a synthetic array for use in AOA estimation. However, these methods have drawbacks that limit their applicability. As an example, in one implementation, users cannot carry the precise moving motor that one of the methods requires to synthesize the antenna array. It should, however, be noted that using a known constant speed rotating motor comes from the inherent restriction of the MUSIC algorithm (the requirement that the sensor position has to be precisely calibrated). When implementing the MUSIC AOA estimation algorithm, the entire array manifold (phase, gain, and sensor positions) has to be perfectly known. Instead of using a precisely moving motor, other researchers have extended the synthetic array idea by using external sensors, namely Inertial Measurements Units (IMU) which consists of accelerometers and gyroscopes, as a potential solution. Instead of using a predefined array shape, The external sensors are used to estimate the trajectory of the antenna in the synthetic array. Unfortunately, this solution still has issues and shortcomings. Trajectory estimation by the IMU is restricted to the level of accuracy that is dictated by the class of IMU and type of motion of the trajectory. On the other hand, the element position perturbation that the MUSIC algorithm can tolerate depends on the wavelength of the frontwaves. Experimental results obtained by using signals in 1.5 GHz band (20 cm wavelength) revealed acceptable results of trajectory estimation when using medium-cost IMUs. However, such results were only for tightly controlled trajectories which had predefined and gentle motions at a constant speed. Truly arbitrary trajectories were not tested and were noted as being quite difficult to estimate.
Based on the above, there is therefore a need for systems and methods that mitigate if not overcome the shortcomings of the prior art.